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In electrical system, strong evolutions are under way that will change deeply the organisation of the whole sector in the short and long term horizon: quick development of renewable technology, volatile and unpredictable production, costly investments in a difficult economic context, competing environment, emission market, new usages of electric vehicles... The simulation and analysis of the evolution of the electrical system is fundamental for better sustaining the energetic transition to a Clean Energy World but it is challenging because of various sources of uncertainties and their interdependence, the interaction between actors and between decisions. The aim of this event is to gather practitioners and academic people, experts in mathematical modeling and numerical simulation applied to energy, to present the most recent advances in these fields.

Open-source stochastic optimization library

The STochastic OPTimization library (StOpt) aims at providing tools in C++ for solving some stochastic optimization problems encountered in finance or in the industry. A python binding is available for some C++ objects provided permitting to easily solve an optimization problem by regression.

Different methods are available : dynamic programming methods based on Monte Carlo with regressions (global, local and sparse regressors), for underlying states following; some uncontrolled Stochastic Differential Equations ; Semi-Lagrangian methods for Hamilton Jacobi Bellman general equations for underlying states following some controlled Stochastic Differential Equations and Stochastic Dual Dynamic Programming methods to deal with stochastic stocks management problems in high dimension

Derniers rapports de recherche

Based on empirical evidence of fast mean-reverting spikes, we model electricity price processes as the sum of a continuous Itö semimartingale and a a mean-reverting compound Poisson process. In a first part, we investigate the estimation of the two parameters of the Poisson process from discrete observations and establish asymptotic efficiency in various asymptotic settings. In a second...

Kernel density estimation and kernel regression are powerful but computationally expensive techniques: a direct evaluation of kernel density estimates at M evaluation points given N input sample points requires a quadratic O(MN) operations, which is prohibitive for large scale problems. For this reason, approximate methods such as binning with Fast Fourier Transform or the...

We extend a recently developed method to solve semi-linear PDEs to the case of a degenerated diffusion. Being a pure Monte Carlo method it does not suer from the so called curse of dimensionality and it can be used to solve problems that were out of reach so far. We give some results of...